Want to build the "plumbing" that powers not just analytics, but the next generation of AI applications? ⚙️?

? What do we offer:

- Projects ranging from massive batch processing to real-time streaming and event-driven architectures.
- Exposure to the cutting edge of AI Engineering: integrating Vector Databases and preparing unstructured data (text, images).
- Opportunity to work with top-tier open-source orchestration and processing tools (Airflow, Spark, Kafka).
- A culture of continuous learning

??? Responsibilities:

- Partner with the CTO and leadership to set the Intelligence strategy and roadmap; own the execution
- Build, hire, and develop the Intelligence team — set the bar for craft, shape the operating cadence, and build the collaboration patterns with product, platform, and engineering
- Stand up the canonical data substrate: schema discipline, tenancy isolation, data contracts, lineage, and governance that AI/ML workloads run cleanly against
- Stand up the ML and AI platform: model lifecycle, feature store, vector store, training and serving infrastructure, and MLOps practice
- Lead the learning and reasoning capabilities of the platform: RAG architectures, agentic data systems, knowledge graphs, and the patterns that let Stratus's data compound into platform intelligence
- Develop and drive evaluation frameworks measuring model quality, agent reliability, drift, and platform effectiveness — make AI workloads observable to engineering, product, and customer success
- Drive the build-vs-buy posture for the AI/ML stack; set production readiness standards for AI workloads in close collaboration with the platform team
- Partner with product on the AI use case portfolio; engage directly with customers when needed to ground Intelligence decisions in real workflow problems

? If you have:

- AI-driven leadership posture:

you personally set the bar for AI-augmented practice — you build with AI tools,



expect your team to, and know how to distinguish what's ready to ship from what still needs a human call
- 10+ years of professional experience in AI/ML, data engineering, or data science, with 4+ years in formal leadership roles (Senior Manager, Director, or Head of) at a B2B SaaS or AI/ML platform company
- Demonstrated track record of building and leading AI/ML or data teams of 5–15 people, with a strong hiring track record in the AI/ML market within the last two to three years
- Deep technical credibility across the modern AI/ML stack: data platforms (Postgres, pgvector, MongoDB or equivalent), ML platforms (training, serving, MLOps), and generative AI (LLMs, embeddings, RAG, fine-tuning, evals)
- Experience shipping production ML and AI workloads to enterprise customers with the trust patterns that come with it: evals, observability, drift detection, confidence scoring
- Excellent communication across all audiences — engineers, product, executives, and customers; strong cross-functional partnership instincts with product, engineering, and customer-facing teams

? It's a plus:

- Experience in construction tech, MEP, BIM, AEC, or other CAD and engineering workflow domains
- Background in AI security and threat modeling: prompt injection, data exfiltration, agent abuse, tenant isolation for AI workloads
- Experience with Azure-native AI architecture (Azure ML, Azure AI Foundry, AKS)
- Prior experience at a Series B or growth-stage company navigating the transition from product-market fit to scale
- Background in regulated or enterprise sales motions where compliance, security, and SLA discipline are non-negotiable

This call is made within the framework of Law ****** on the Promotion of Employment for Persons with Disabilities, including individuals registered in the National Registry of Persons with Disabilities of the Ministry of Social Development

? Lead Data Engineer (Buenos Aires)
? Marvik
? Buenos Aires

📌 Lead Data Engineer (Buenos Aires)
🏢 Importante empresa del sector
📍 Buenos Aires